Book chapter
Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model
Matrices, Statistics and Big Data, pp.127-144
Contributions to Statistics, Springer International Publishing
08/02/2019
DOI: 10.1007/978-3-030-17519-1_10
Abstract
Functional data become increasingly popular with the rapid technological development in data collection and storage. In this study, we consider both scalar and functional predictors for a positive scalar response under the partially linear multiplicative model. A loss function based on the relative errors is adopted, which provides a useful alternative to the classic methods such as the least squares. Penalization is used to detect the true structure of the model. The proposed method can not only identify the significant scalar variables but also select the basis functions (on which the functional variable is projected) that contribute the response. Both estimation and selection consistency properties are rigorously established. Simulation is conducted to investigate the finite sample performance of the proposed method. We analyze the Tecator data to demonstrate application of the proposed method.
Details
- Title: Subtitle
- Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model
- Creators
- Tao Zhang - Guangxi University of Science and TechnologyYuan Huang - University of IowaQingzhao Zhang - Xiamen UniversityShuangge Ma - Yale UniversityS. Ejaz Ahmed
- Resource Type
- Book chapter
- Publication Details
- Matrices, Statistics and Big Data, pp.127-144
- Publisher
- Springer International Publishing; Cham
- Series
- Contributions to Statistics
- DOI
- 10.1007/978-3-030-17519-1_10
- ISSN
- 1431-1968
- Language
- English
- Date published
- 08/02/2019
- Academic Unit
- Biostatistics
- Record Identifier
- 9984364537102771
Metrics
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